The prediction error for binary classification is possibly the simplest measure available. It is the number of training examples that are misclassified, divided by the total number of examples. Similarly, accuracy is the number of correctly classified examples divided by the total examples.

We can calculate the accuracy of our models in our training data by making predictions on each input feature and comparing them to the true label. We will sum up the number of correctly classified instances, and divide this by the total number of data points to get the average classification accuracy.

val lrTotalCorrect = data.map { point => if (lrModel.predict(point.features) == point.label) 1 else 0 }.sum val lrAccuracy ...